Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background

Abstract

Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes, and propose a samples normalized cross-correlations-based auto-regressive moving average parameter estimation method effective in noisy environments. Simulation results of auto-regressive moving average parameter estimation exhibit good performance.